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Sounding data collected during the WINTRE-MIX project field phase are included in this dataset. This dataset has soundings from the University of Colorado (CU) DOWs, McGill University at Gault, St Jean sur Richelieu, University at Albany (UA) DOWs, Université du Québec à Montréal (UQAM), and UA Essex sites. data file names are of the form "upperair.sounding.YYYYMMDDHHMM.siteName.[txt or csv]" where the YYYMMDDHHMM indicates the date and time of the sounding and the siteName indicates the site source and location. See the documentation for more information on this dataset.

Abstract Climate changes over the past two millennia in the central part of the Gulf of St. Lawrence are documented in this paper with the aim of determining and understanding the natural climate variability and the impact of anthropogenic forcing at a regional scale. The palynological content (dinocysts, pollen, and spores) of the composite marine sediment core MSM46-03 collected in the Laurentian Channel was used to reconstruct oceanographic and climatic changes with a multidecadal temporal resolution. Sea-surface conditions, including summer salinity and temperature, sea-ice cover, and primary productivity, were reconstructed from dinocyst assemblages. Results revealed a remarkable cooling trend of about 4°C after 1230 cal yr BP (720 CE) and a culmination with a cold pulse dated to 170–40 cal yr BP (1780–1910 CE), which likely corresponds to the regional signal of the Little Ice Age. This cold interval was followed by a rapid warming of about 3°C. In the pollen assemblages, the decrease of Pinus abundance over the past 1700 yr suggests changes in wind regimes, likely resulting from increased southerly incursions of cold and dry Arctic air masses into southeastern Canada.

Although rice paddy fields are one of the world’s largest anthropogenic sources of methane CH4, the budget of ecosystem CH4 and its’ controls in rice paddies remain unclear. Here, we analyze seasonal dynamics of direct ecosystem-scale measurements of CH4 flux in a rice-wheat rotation agroecosystem over 3 consecutive years. Results showed that the averaged CO2 uptakes and CH4 emissions in rice seasons were 2.2 and 20.9 folds of the wheat seasons, respectively. In sum, the wheat-rice rotation agroecosystem acted as a large net C sink (averaged 460.79 g C m−2) and a GHG (averaged 174.38 g CO2eq m−2) source except for a GHG sink in one year (2016) with a very high rice seeding density. While the linear correlation between daily CH4 fluxes and gross ecosystem productivity (GEP) was not significant for the whole rice season, daily CH4 fluxes were significantly correlated to daily GEP both before (R2: 0.52–0.83) and after the mid-season drainage (R2: 0.71–0.79). Furthermore, the F partial test showed that GEP was much greater than that of any other variable including soil temperature for the rice season in each year. Meanwhile, the parameters of the best-fit functions between daily CH4 fluxes and GEP shifted between rice growth stages. This study highlights that GEP is a good predictor of daily CH4 fluxes in rice paddies.

Quantifying the characteristics of urban expansion as well as influencing factors is essential for the simulation and prediction of urban expansion. In this study, we extracted the built-up regions of 14 central cities in the Hunan province using the DMSP-OLS night light remote sensing datasets from 1992 to 2018, and evaluated the spatial and temporal characteristics of the built-up regions in terms of the area, expansion speed, and main expansion direction. The backpropagation (BP) neural network and autoregressive integrated moving average (ARIMA) model were used to predict the area of the built-up regions from 2019 to 2026. The model predictions were based on the GDP, ratio of the secondary industry output to the GDP, ratio of the tertiary industry output to the GDP, year-end urban population, and urban road area. The results demonstrated that the built-up area and expansion speed of the central cities in the eastern part of the Hunan province were significantly higher than those in the western part. The main expansion directions of the 14 central cities were east and south. The urban road area, year-end urban population, and GDP were the main driving factors of the expansion. The urban expansion model based on the BP neural network provided a high prediction accuracy (R = 0.966). It was estimated that the total area of urban built-up regions in the Hunan province will reach 2463.80 km2 by 2026. These findings provide a new perspective for predicting urban areas rapidly and simply, and it also provides a useful reference for studying the spatial expansion characteristics of central cities and formulating a sustainable urban development strategy during the 14th Five-Year Plan of China.

Abstract The importance of resolving mesoscale air‐sea interactions to represent cyclones impacting the East Coast of Australia, the so‐called East Coast Lows (ECLs), is investigated using the Australian Regional Coupled Model based on NEMO‐OASIS‐WRF (NOW) at resolution. The fully coupled model is shown to be capable of reproducing correctly relevant features such as the seasonality, spatial distribution and intensity of ECLs while it partially resolves mesoscale processes, such as air‐sea feedbacks over ocean eddies and fronts. The mesoscale thermal feedback (TFB) and the current feedback (CFB) are shown to influence the intensity of northern ECLs (north of ), with the TFB modulating the pre‐storm sea surface temperature (SST) by shifting ECL locations eastwards and the CFB modulating the wind stress. By fully uncoupling the atmospheric model of NOW, the intensity of northern ECLs is increased due to the absence of the cold wake that provides a negative feedback to the cyclone. The number of ECLs might also be affected by the air‐sea feedbacks but large interannual variability hampers significant results with short‐term simulations. The TFB and CFB modify the climatology of SST (mean and variability) but no direct link is found between these changes and those noticed in ECL properties. These results show that the representation of ECLs, mainly north of , depend on how air‐sea feedbacks are simulated. This is particularly important for atmospheric downscaling of climate projections as small‐scale SST interactions and the effects of ocean currents are not accounted for. , Plain Language Summary Air‐sea interactions occur at a variety of spatial scales, including those of the size of ocean eddies. Such interactions are partially resolved in the Australian Regional Coupled Model used to simulate the cyclones impacting the East Coast of Australia, the so‐called East Coast Lows (ECLs). The effect of different feedbacks between the ocean and the atmosphere, including those due to mechanical and thermal exchanges over ocean eddies, are tested on the properties of ECLs. Significant effects are found on the intensity of ECLs north of , with also potential effects on the number of ECLs. The air‐sea feedbacks modify the climatology of sea surface temperature, with no direct link to ECL changes. Such experiments eventually demonstrate that small‐scale air‐sea feedbacks may matter for representing current Australian climate and its change in the future. , Key Points High‐resolution regional coupled modeling can simulate key features of East Australian cyclones Cyclone intensity is sensitive to mechanical and thermal air‐sea feedbacks at mesoscales Coupled and atmosphere‐only models mainly differ in simulating cyclone properties north of

A trait-based approach is an effective way to quantify plant adaptation strategies in response to changing environments. Single trait variations have been well depicted before; however, multi-trait covariations and their roles in shaping plant adaptation strategies along aridity gradients remain unclear. The purpose of this study was to reveal multi-trait covariation characteristics, their controls and their relevance to plant adaptation strategies. Using eight relevant plant functional traits and multivariate statistical approaches, we found the following: (1) the eight studied traits show evident covariation characteristics and could be grouped into four functional dimensions linked to plant strategies, namely energy balance, resource acquisition, resource investment and water use efficiency; (2) leaf area (LA) together with traits related to the leaf economic spectrum, including leaf nitrogen content per area (Narea), leaf nitrogen per mass (Nmass) and leaf dry mass per area (LMA), covaried along the aridity gradient (represented by the moisture index, MI) and dominated the trait–environmental change axis; (3) together, climate, soil and family can explain 50.4% of trait covariations; thus, vegetation succession along the aridity gradient cannot be neglected in trait covariations. Our findings provide novel perspectives toward a better understanding of plant adaptations to arid conditions and serve as a reference for vegetation restoration and management programs in arid regions.

Abstract. Starphotometry, the night-time counterpart of sunphotometry, has not yet achieved the commonly sought observational error level of 1 %: a spectral optical depth (OD) error level of 0.01. In order to address this issue, we investigate a large variety of systematic (absolute) uncertainty sources. The bright-star catalogue of extraterrestrial references is noted as a major source of errors with an attendant recommendation that its accuracy, particularly its spectral photometric variability, be significantly improved. The small field of view (FOV) employed in starphotometry ensures that it, unlike sun- or moonphotometry, is only weakly dependent on the intrinsic and artificial OD reduction induced by scattering into the FOV by optically thin clouds. A FOV of 45 arcsec (arcseconds) was found to be the best trade-off for minimizing such forward-scattering errors concurrently with flux loss through vignetting. The importance of monitoring the sky background and using interpolation techniques to avoid spikes and to compensate for measurement delay was underscored. A set of 20 channels was identified to mitigate contamination errors associated with stellar and terrestrial atmospheric gas absorptions, as well as aurora and airglow emissions. We also note that observations made with starphotometers similar to our High Arctic instrument should be made at high angular elevations (i.e. at air masses less than 5). We noted the significant effects of snow crystal deposition on the starphotometer optics, how pseudo OD increases associated with this type of contamination could be detected, and how proactive techniques could be employed to avoid their occurrence in the first place. If all of these recommendations are followed, one may aspire to achieve component errors that are well below 0.01: in the process, one may attain a total 0.01 OD target error.

Urbanization can induce environmental changes such as the urban heat island effect, which in turn influence the terrestrial ecosystem. However, the effect of urbanization on the phenology of subtropical vegetation remains relatively unexplored. This study analyzed the changing trend of vegetation photosynthetic phenology in Dongting Lake basin, China, and its response to urbanization using nighttime light and chlorophyll fluorescence datasets. Our results indicated the start of the growing season (SOS) of vegetation in the study area was significantly advanced by 0.70 days per year, whereas the end of the growing season (EOS) was delayed by 0.24 days per year during 2000–2017. We found that urbanization promoted the SOS advance and EOS delay. With increasing urbanization intensity, the sensitivity of SOS to urbanization firstly increased then decreased, while the sensitivity of EOS to urbanization decreased with urbanization intensity. The climate sensitivity of vegetation phenology varied with urbanization intensity; urbanization induced an earlier SOS by increasing preseason minimum temperatures and a later EOS by increasing preseason precipitation. These findings improve our understanding of the vegetation phenology response to urbanization in subtropical regions and highlight the need to integrate human activities into future vegetation phenology models.

Abstract. Using the Max Planck Institute Grand Ensemble (MPI-GE) with 200 members for the historical simulation (1850–2005), we investigate the impact of the spatial distribution of volcanic aerosols on the El Niño–Southern Oscillation (ENSO) response. In particular, we select three eruptions (El Chichón, Agung and Pinatubo) in which the aerosol is respectively confined to the Northern Hemisphere, the Southern Hemisphere or equally distributed across the Equator. Our results show that relative ENSO anomalies start at the end of the year of the eruption and peak in the following one. We especially found that when the aerosol is located in the Northern Hemisphere or is symmetrically distributed, relative El Niño-like anomalies develop, while aerosol distribution confined to the Southern Hemisphere leads to a relative La Niña-like anomaly. Our results point to the volcanically induced displacement of the Intertropical Convergence Zone (ITCZ) as a key mechanism that drives the ENSO response, while suggesting that the other mechanisms (the ocean dynamical thermostat and the cooling of tropical northern Africa or the Maritime Continent) commonly invoked to explain the post-eruption ENSO response may be less important in our model.

Methane (CH4) is one of the three most important greenhouse gases. To date, observations of ecosystem-scale methane (CH4) fluxes in forests are currently lacking in the global CH4 budget. The environmental factors controlling CH4 flux dynamics remain poorly understood at the ecosystem scale. In this study, we used a state-of-the-art eddy covariance technique to continuously measure the CH4 flux from 2016 to 2018 in a subtropical forest of Zhejiang Province in China, quantify the annual CH4 budget and investigate its control factors. We found that the total annual CH4 budget was 1.15 ± 0.28~4.79 ± 0.49 g CH4 m−2 year−1 for 2017–2018. The daily CH4 flux reached an emission peak of 0.145 g m−2 d−1 during winter and an uptake peak of −0.142 g m−2 d−1 in summer. During the whole study period, the studied forest region acted as a CH4 source (78.65%) during winter and a sink (21.35%) in summer. Soil temperature had a negative relationship (p < 0.01; R2 = 0.344) with CH4 flux but had a positive relationship with soil moisture (p < 0.01; R2 = 0.348). Our results showed that soil temperature and moisture were the most important factors controlling the ecosystem-scale CH4 flux dynamics of subtropical forests in the Tianmu Mountain Nature Reserve in Zhejiang Province, China. Subtropical forest ecosystems in China acted as a net source of methane emissions from 2016 to 2018, providing positive feedback to global climate warming.

Anthropogenic climate change is currently driving environmental transformation on a scale and at a pace that exceeds historical records. This represents an undeniably serious challenge to existing social, political, and economic systems. Humans have successfully faced similar challenges in the past, however. The archaeological record and Earth archives offer rare opportunities to observe the complex interaction between environmental and human systems under different climate regimes and at different spatial and temporal scales. The archaeology of climate change offers opportunities to identify the factors that promoted human resilience in the past and apply the knowledge gained to the present, contributing a much-needed, long-term perspective to climate research. One of the strengths of the archaeological record is the cultural diversity it encompasses, which offers alternatives to the solutions proposed from within the Western agro-industrial complex, which might not be viable cross-culturally. While contemporary climate discourse focuses on the importance of biodiversity, we highlight the importance of cultural diversity as a source of resilience.

Understanding the impacts of nitrogen (N) addition on soil respiration (RS) and its temperature sensitivity (Q10) in tropical forests is very important for the global carbon cycle in a changing environment. Here, we investigated how RS respond to N addition in a tropical montane rainforest in Southern China. Four levels of N treatments (0, 25, 50, and 100 kg N ha−1 a−1 as control (CK), low N (N25), moderate N (N50), and high N (N100), respectively) were established in September 2010. Based on a static chamber-gas chromatography method, RS was measured from January 2015 to December 2018. RS exhibited significant seasonal variability, with low RS rates appeared in the dry season and high rates appeared in the wet season regardless of treatment. RS was significantly related to the measured soil temperature and moisture. Our results showed that soil RS increased after N additions, the mean annual RS was 7% higher in N25 plots, 8% higher in N50 plots, and 11% higher in N100 plots than that in the CK plots. However, the overall impacts of N additions on RS were statistically insignificant. For the entire study period, the CK, N25, N50, and N100 treatments yielded Q10 values of 2.27, 3.45, 4.11, and 2.94, respectively. N addition increased the temperature sensitivity (Q10) of RS. Our results suggest that increasing atmospheric N deposition may have a large impact on the stimulation of soil CO2 emissions from tropical rainforests in China.

Abstract. Previous studies based on multiple paleoclimate archives suggested a prominent intensification of the South Asian Monsoon (SAM) during the mid-Holocene (MH, ∼6000 years before present). The main forcing that contributed to this intensification is related to changes in the Earth's orbital parameters. Nonetheless, other key factors likely played important roles, including remote changes in vegetation cover and airborne dust emission. In particular, northern Africa also experienced much wetter conditions and a more mesic landscape than today during the MH (the so-called African Humid Period), leading to a large decrease in airborne dust globally. However, most modeling studies investigating the SAM changes during the Holocene overlooked the potential impacts of the vegetation and dust emission changes that took place over northern Africa. Here, we use a set of simulations for the MH climate, in which vegetation over the Sahara and reduced dust concentrations are considered. Our results show that SAM rainfall is strongly affected by Saharan vegetation and dust concentrations, with a large increase in particular over northwestern India and a lengthening of the monsoon season. We propose that this remote influence is mediated by anomalies in Indian Ocean sea surface temperatures and may have shaped the evolution of the SAM during the termination of the African Humid Period.

In sub-Saharan Africa growing season precipitation is affected by climate change. Due to this, in Cameroon, it is uncertain how some crops are vulnerable to growing season precipitation. Here, an assessment of the vulnerability of maize, millet, and rice to growing season precipitation is carried out at a national scale and validated at four sub-national scales/sites. The data collected were historical yield, precipitation, and adaptive capacity data for the period 1961–2019 for the national scale analysis and 1991–2016 for the sub-national scale analysis. The crop yield data were collected for maize, millet, and rice from FAOSTAT and the global yield gap atlas to assess the sensitivity both nationally and sub-nationally. Historical data on mean crop growing season and mean annul precipitation were collected from a collaborative database of UNDP/Oxford University and the climate portal of the World Bank to assess the exposure both nationally and sub-nationally. To assess adaptive capacity, literacy, and poverty rate proxies for both the national and regional scales were collected from KNOEMA and the African Development Bank. These data were analyzed using a vulnerability index that is based on sensitivity, exposure, and adaptive capacity. The national scale results show that millet has the lowest vulnerability index while rice has the highest. An inverse relationship between vulnerability and adaptive capacity is observed. Rice has the lowest adaptive capacity and the highest vulnerability index. Sub-nationally, this work has shown that northern maize is the most vulnerable crop followed by western highland rice. This work underscores the fact that at different scales, crops are differentially vulnerable due to variations in precipitation, temperature, soils, access to farm inputs, exposure to crop pest and variations in literacy and poverty rates. Therefore, caution should be taken when transitioning from one scale to another to avoid generalization. Despite these differences, in the sub-national scale, western highland rice is observed as the second most vulnerable crop, an observation similar to the national scale observation.

The objective of this study was to estimate economic losses associated with milk performance detriments under different climate scenarios. A dataset containing milk records of Holstein and daily temperature–humidity indexes compiled over 6 yr in two contrasting climatic dairy regions of Quebec [eastern (EQ) and southwestern Quebec (SWQ)] was used to develop equations for modeling milk performance. Milk performance, including milk, fat, protein, and lactose yields of dairy herds (kg·d −1 ), were then projected considering six plausible climate scenarios during a climatic reference period (REF: 1971–2000) and two future periods (FUT1: 2020–2049; FUT2: 2050–2079). Economic losses were assessed by comparing future to reference milk prices based on components. Results indicated that fat and protein yields could decline in the future, thus resulting in economic losses ranging from $5.34 to $7.07 CAD·hL −1 in EQ and from $5.03 to $6.99 CAD·hL −1 in SWQ, depending on the amplitude of future temperature and humidity changes and on whether it is milk quota or cow number which is adjusted to avoid under-quota production. The projected increase in occurrence and duration of heat stress episodes under climate change could result in substantial financial harm for producers, thereby reinforcing the necessity of implementing heat-abatement strategies on dairy farms.